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Water Network Tool for Resilience (WNTR)

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/WNTR
https://doi.org/10.11578/dc.20171025.1707

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Abstract

The Water Network Tool for Resilience (WNTR) is an open source Python package designed to simulate and analyze resilience of water distribution networks. The United States Environmental Protection Agency, in partnership with Sandia National Laboratories, developed WNTR to integrate critical aspects of resilience modeling for water distribution networks into a single software framework. The software includes capability to: • Generate water network models • Modify network structure and operations • Assign fragility and survival curves to network components • Model disruptive events such as power outages, earthquakes, fires, pipe breaks, and contamination incidents • Model response and repair strategies • Simulate hydraulics and water quality • Evaluate resilience using a wide range of metrics • Integrate dependency with other critical infrastructure and supply chains • Analyze results and generate graphics SAND2019-450 M
Developers:
Villa, Daniel ;  Hart, David [1] ;  Klise, Katherine [1] ;  Bynum, Michael [1] ;  Laird, Carl [1] ;  Seth, Arpan [2] ;  Bynum, Michael ;  Moriarty, Dylan ;  Hogge, Joseph ;  Bonney, Kirk
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
Release Date:
2023-02-15
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
C++
C
Python
Version:
1.0
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
3818
Site Accession Number:
SCR#2043.2
Research Org.:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/sandialabs/WNTR
https://doi.org/10.11578/dc.20171025.1707

SAVE / SHARE



SNL Badge

Citation Formats

Villa, Daniel, Hart, David, Klise, Katherine A., Bynum, Michael L., Laird, Carl D., Seth, Arpan, Bynum, Michael, Moriarty, Dylan, Hogge, Joseph, and Bonney, Kirk. Water Network Tool for Resilience (WNTR). Computer Software. https://github.com/sandialabs/WNTR. USDOE. 15 Feb. 2023. Web. doi:10.11578/dc.20171025.1707.
Villa, Daniel, Hart, David, Klise, Katherine A., Bynum, Michael L., Laird, Carl D., Seth, Arpan, Bynum, Michael, Moriarty, Dylan, Hogge, Joseph, & Bonney, Kirk. (2023, February 15). Water Network Tool for Resilience (WNTR). [Computer software]. https://github.com/sandialabs/WNTR. https://doi.org/10.11578/dc.20171025.1707.
Villa, Daniel, Hart, David, Klise, Katherine A., Bynum, Michael L., Laird, Carl D., Seth, Arpan, Bynum, Michael, Moriarty, Dylan, Hogge, Joseph, and Bonney, Kirk. "Water Network Tool for Resilience (WNTR)." Computer software. February 15, 2023. https://github.com/sandialabs/WNTR. https://doi.org/10.11578/dc.20171025.1707.
@misc{ doecode_3818,
title = {Water Network Tool for Resilience (WNTR)},
author = {Villa, Daniel and Hart, David and Klise, Katherine A. and Bynum, Michael L. and Laird, Carl D. and Seth, Arpan and Bynum, Michael and Moriarty, Dylan and Hogge, Joseph and Bonney, Kirk},
abstractNote = {The Water Network Tool for Resilience (WNTR) is an open source Python package designed to simulate and analyze resilience of water distribution networks. The United States Environmental Protection Agency, in partnership with Sandia National Laboratories, developed WNTR to integrate critical aspects of resilience modeling for water distribution networks into a single software framework. The software includes capability to: • Generate water network models • Modify network structure and operations • Assign fragility and survival curves to network components • Model disruptive events such as power outages, earthquakes, fires, pipe breaks, and contamination incidents • Model response and repair strategies • Simulate hydraulics and water quality • Evaluate resilience using a wide range of metrics • Integrate dependency with other critical infrastructure and supply chains • Analyze results and generate graphics SAND2019-450 M},
doi = {10.11578/dc.20171025.1707},
url = {https://doi.org/10.11578/dc.20171025.1707},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20171025.1707}},
year = {2023},
month = {feb}
}